1. Field of the Art
The invention is in the field of information search, including Internet search, vertical search, ecommerce related search and similar types of information search. The invention assists end users in forming meaningful and interesting search queries, including natural-language queries, and discovering what type of data is available in the system.
2. Discussion of the State of the Art
Certain computer applications require the user to express themselves in natural language or a subset or simplified version of natural language. Examples of such applications are search engines and computer interactive dialog systems. Natural language as an interface has its advantages and disadvantages. The main advantage is that if it is utilized to its maximal potential a system based on natural language enables the user to express most anything that a human being is able to express. However, it is this open ended flexibility that also poses problems with these kinds of interfaces. In a regular graphical user interface (UI) a user is usually able to ascertain what functionality is available to them by exploring the different visual components of the UI (the menus, buttons, etc). In contrast in a natural language system the user does not have context clues to let them know what functionality is available to them. This poses a fundamental problem as the user is expected to know a priori what functionality is available and to be able and formulate natural language that the system “understands” to access the functionality.
Certain technological aids have been developed to facilitate this process. One such approach is auto complete. In auto complete as a user enters text the system responds by offering possible completions to what they have typed so far. This addresses part of the problem, leading the user to system valid phrases and shortening their time typing. While auto complete is useful for what it does it does not help with fundamental exploration problem. The user is still expected to have an understanding of what options are generally available to them and auto complete makes it easier to get to those options. Furthermore, given an external context the user does not know which queries actually generate relevant results; for instance a search for “romantic French restaurant” in one city may produce many results, but may not produce any in another city, etc.
An additional problem is related to the entry of specific, quantitative information, like dates, prices, no. of units, etc. This type of interaction is in easier and less error-prone by selecting from a visual representation (e.g., calendar, bar chart) than by entering natural language.
An additional problem relates to the completeness of forming a query. Some queries are only relevant if a certain set of values was expressed. Often end-users have a hard time realizing what data is required and what additional data is helpful. For instance consider a hotel search—a query without dates is often of little value as the dates define the availability (hence the set of relevant hotels) and the prices.
What is needed is an enhanced UI that combines the flexibility of natural language with the context and exploration ability of a graphical user interface.